Non Maximum Suppression: Theory and Implementation in PyTorch

Non Maximum Suppression (NMS) is a technique used in numerous computer vision tasks. It is a class of algorithms to select one entity (e.g., bounding boxes) out of many overlapping entities. We can choose the selection criteria to arrive at the desired results. The criteria are most commonly some form of probability number and some form of overlap measure (e.g. Intersection over Union).

Object Detection Metrics With Worked Example

Average Precision (AP) and mean Average Precision (mAP) are the most popular metrics used to evaluate object detection models such as Faster R_CNN, Mask R-CNN, YOLO among others. The same metrics have also been used the evaluate submissions in competitions like COCO and PASCAL VOC challenges.